tensorflow/models · error · ValueError

The inner_dim of f{self.__class__} must be an even integer.

Error message

The inner_dim of f{self.__class__} must be an even integer. However, inner_dim is f{inner_dim}

What it means

Error "The inner_dim of f{self.__class__} must be an even integer. However, inner_dim is f{inner_dim}" thrown in tensorflow/models.

Source

Thrown at official/projects/roformer/roformer_encoder_block.py:88

      norm_first: Whether to normalize inputs to attention and intermediate
        dense layers. If set False, output of attention and intermediate dense
        layers is normalized.
      norm_epsilon: Epsilon value to initialize normalization layers.
      output_dropout: Dropout probability for the post-attention and output
        dropout.
      attention_dropout: Dropout probability for within the attention layer.
      inner_dropout: Dropout probability for the first Dense layer in a
        two-layer feedforward network.
      attention_initializer: Initializer for kernels of attention layers. If set
        `None`, attention layers use kernel_initializer as initializer for
        kernel.
      attention_axes: axes over which the attention is applied. `None` means
        attention over all axes, but batch, heads, and features.
      **kwargs: keyword arguments.
    """
    super().__init__(**kwargs)
    if inner_dim % 2 != 0:
      raise ValueError(f"The inner_dim of f{self.__class__} must be an even "
                       f"integer. However, inner_dim is f{inner_dim}")
    self._num_heads = num_attention_heads
    self._inner_dim = inner_dim
    self._inner_activation = inner_activation
    self._attention_dropout = attention_dropout
    self._attention_dropout_rate = attention_dropout
    self._output_dropout = output_dropout
    self._output_dropout_rate = output_dropout
    self._output_range = output_range
    self._kernel_initializer = tf_keras.initializers.get(kernel_initializer)
    self._bias_initializer = tf_keras.initializers.get(bias_initializer)
    self._kernel_regularizer = tf_keras.regularizers.get(kernel_regularizer)
    self._bias_regularizer = tf_keras.regularizers.get(bias_regularizer)
    self._activity_regularizer = tf_keras.regularizers.get(activity_regularizer)
    self._kernel_constraint = tf_keras.constraints.get(kernel_constraint)
    self._bias_constraint = tf_keras.constraints.get(bias_constraint)
    self._use_bias = use_bias
    self._norm_first = norm_first

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/projects/roformer/roformer_encoder_block.py:88 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24). Data as JSON: /api/errors/f5d2a265e0661058. Report an issue: GitHub.